Overview
The Show Notes calculator allows you to add textual annotations, documentation, and explanatory notes directly within your process mining analysis. Unlike other calculators that analyze event log data, this calculator simply displays your custom text content without performing any calculations.
This calculator is ideal for documenting analysis decisions, providing context for dashboard viewers, explaining methodology, or adding commentary that travels with your analysis configuration. It serves as an embedded documentation tool within your mindzieStudio workflow.
Common Uses
- Document analysis assumptions, methodology, or data preparation steps
- Provide explanations for KPIs, metrics, or visualizations on dashboards
- Add comments explaining why specific filters or calculations were applied
- Document known data issues, limitations, or caveats
- Share insights, observations, or recommendations with team members
- Create section headers or dividers in complex dashboards
- Add data source attribution, timestamps, or disclaimers
Settings
Notes: Enter the text content you want to display. This can be plain text, multi-line explanations, or formatted content (if your rendering environment supports Markdown or HTML).
There are no other configuration options beyond the standard title and description fields. The calculator simply displays whatever text you enter in the Notes field.
Examples
Example 1: Documenting Filter Strategy
Scenario: You want to explain to dashboard viewers why you filtered out cases before a specific date, so they understand the scope of the analysis.
Settings:
- Title: "Analysis Scope Note"
- Notes: "Filtering Strategy: We exclude cases that started before 2024-01-01 because the system underwent a major upgrade on that date, changing the activity structure. Including older cases would skew the variant analysis."
Output:
The calculator displays your note as a text block on the dashboard, clearly explaining the filtering rationale.
Insights: This documentation ensures that anyone viewing the analysis understands why the data was filtered, preventing confusion about why older cases aren't included. It preserves the reasoning behind analytical decisions even when the original analyst isn't available to explain.
Example 2: Dashboard Section Header
Scenario: You're building a comprehensive dashboard and want to add a clear section header to introduce the payment performance metrics.
Settings:
- Title: "Payment Performance Section"
- Notes: "Payment Performance Analysis\n\nThis section tracks on-time payment rates and identifies late payment patterns. Data source: SAP ERP, updated daily at 2 AM UTC."
Output:
The note appears as a formatted text block that introduces the section, providing context about what metrics follow and when the data was last refreshed.
Insights: Section headers make complex dashboards easier to navigate and understand, especially for stakeholders who didn't create the analysis. Including the data source and refresh time helps users assess data freshness.
Example 3: Analysis Methodology Documentation
Scenario: You've completed a root cause analysis and want to document the approach you used so that the methodology is transparent.
Settings:
- Title: "Root Cause Methodology"
- Notes: "Root Cause Analysis Methodology:\n1. Identified cases with duration over 30 days (90th percentile)\n2. Applied decision tree to find correlating attributes\n3. Validated findings with business stakeholders\n4. Recommended process improvements based on top 3 root causes"
Output:
A clear step-by-step explanation of the analysis methodology appears alongside your root cause analysis results.
Insights: Documenting methodology makes your analysis reproducible and transparent. Other analysts can understand your approach, and stakeholders can assess the rigor of your analysis.
Example 4: Data Quality Disclaimer
Scenario: You know there's incomplete data for one department during a specific time period and want to warn dashboard viewers about this limitation.
Settings:
- Title: "Data Quality Notice"
- Notes: "IMPORTANT: This dataset contains incomplete data for Department X due to a system integration issue during March 2024. Department X metrics should be interpreted with caution. Issue resolved as of April 1, 2024."
Output:
A prominently displayed warning appears on the dashboard, alerting viewers to the data quality issue.
Insights: Proactively documenting data quality issues prevents misinterpretation of results and builds trust with stakeholders by being transparent about limitations.
Example 5: Performance Baseline Documentation
Scenario: You're documenting baseline metrics before implementing process improvements so you can measure the impact of changes later.
Settings:
- Title: "Pre-Improvement Baseline"
- Notes: "Invoice Processing Baseline - January 2025\n\nCurrent State:\n- Average processing time: 12.3 days\n- On-time payment rate: 67%\n- Rework rate: 23%\n\nTarget State (by June 2025):\n- Average processing time: under 8 days\n- On-time payment rate: over 85%\n- Rework rate: under 10%"
Output:
A clear comparison of current performance and target metrics appears on the dashboard, establishing the baseline for measuring improvement.
Insights: Documenting baselines and targets creates accountability and makes it easy to measure the success of improvement initiatives. When you review the dashboard in six months, you'll immediately see whether you achieved your goals.
Example 6: Collaboration and Recommendations
Scenario: After analyzing the process, you want to share your key findings and recommendations with the process improvement team.
Settings:
- Title: "Key Findings and Next Steps"
- Notes: "Analysis Findings (Q4 2024):\n\nTop 3 Bottlenecks:\n1. Manager approval step (avg 4.2 days wait time)\n2. Vendor document collection (avg 3.8 days)\n3. Invoice matching errors (affects 18% of cases)\n\nRecommendations:\n- Implement automated approval for orders under $5,000\n- Create vendor portal for document uploads\n- Add validation rules to prevent matching errors\n\nAnalyst: John Smith | Date: 2024-12-15"
Output:
A comprehensive summary of findings and actionable recommendations appears on the dashboard, complete with attribution and date.
Insights: This transforms your dashboard from a simple metrics display into an actionable report that guides improvement efforts. Including analyst name and date creates accountability and helps track the analysis timeline.
Output
The Show Notes calculator displays your text content as-is in a simple text block format. The exact rendering depends on your dashboard environment:
Text Display: The notes appear as plain text or formatted text (if Markdown or HTML rendering is supported).
No Data Processing: Unlike other calculators, this calculator doesn't analyze your event log or display any calculated metrics. It simply shows the static text you configured.
Dashboard Integration: You can add the notes calculator output to your dashboard just like any other calculator. It appears as a text widget or card that can be positioned alongside other metrics and visualizations.
Formatting Options: Depending on your rendering environment, you may be able to use:
- Line breaks (\n) for multi-line text
- Markdown formatting (headers, lists, bold, italic)
- HTML formatting (if supported by the renderer)
The calculator is ideal for creating self-documenting analyses where the context and reasoning travel with the data and calculations.
This documentation is part of the mindzie Studio process mining platform.